Multi-Sensor Optimization Scheduling for Target Tracking Based on PCRLB and a Novel Intercept Probability Factor

In order to improve the survivability of active sensors, the problem of low probability of intercept (LPI) for a multi-sensor network system is studied in this paper. Two kinds of operational requirements are taken into account, the first of which is to ensure the survivability of sensors and the second is to improve the tracking accuracy of targets as much as possible. Firstly, the sensor tracking model and the posterior Carmer-Rao lower bound (PCRLB) of the target are presented to evaluate the sensor tracking benefits in next time. Then, a novel intercept probability factor (IPF) is proposed for multi-sensor multi-target tracking scenarios. At the basis of PCRLB and IPF, a myopic multi-sensor scheduling model for target tracking is set up to control the intercepted probability of sensors and improve the target tracking accuracy. At last, a fast solution algorithm based on an improved particle swarm optimization (PSO) algorithm is given to obtain the optimal scheduling actions. Simulation of experimental results show that the proposed model can effectively control the intercepted risk of every sensor, which can also obtain better target tracking performance than existing multi-sensor scheduling methods.